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About the book

Reviews

anonymous
★★★★★

25 September 2013

Great book, very interesting and a great tool to bridge the gap between A-level Maths statistics modules (such as chi-squared being in s2 and 3) to university level.

Description

Applying mathematical and statistical practices to economics, econometrics enables economists to test theoretical hypotheses with real world data. This Econometrics e-book is available as a free download. It provides simple explanations of key concepts in the field, with numerous examples and clear statistical tables for reference.

Econometrics provides a clear and in-depth introduction to these concepts, and is clearly organized for reference use. Important vocabulary and terminology is highlighted in bold. Each chapter begins with a clear introduction and useful equations are broken down with detailed explanations.

Download the free e-book of Econometrics!

Content

Basics of probability and statistics

Random variables and probability distributions

The multivariate probability distribution function

Characteristics of probability distributions

Basic probability distributions in econometrics

The normal distribution

The t-distribution

The Chi-square distribution

The F-distribution

The simple regression model

The population regression model

Estimation of population parameters

Statistical inference

Hypothesis testing

Confidence interval

Type I and type II errors

The best linear predictor

Model measures

The coefficient of determination (R2)

The adjusted coefficient of determination (Adjusted R2)

The analysis of variance table (ANOVA)

The multiple regression model

Partial marginal effects

Estimation of partial regression coefficients

The joint hypothesis test

Specification

Choosing the functional form

Omission of a relevant variable

Inclusion of an irrelevant variable

Measurement errors

Dummy variables

Intercept dummy variables

Slope dummy variables

Qualitative variables with several categories

Piecewise linear regression

Test for structural differences

Heteroskedasticity and diagnostics

Consequences of using OLS

Detecting heteroskedasticity

Remedial measures

Autocorrelation and diagnostics

Definition and the nature of autocorrelation

Consequences

Detection of autocorrelation

Remedial measures

Multicollinearity and diagnostics

Consequences

Measuring the degree of multicollinearity

Remedial measures

Simultaneous equation models

Introduction

The structural and reduced form equation

Identification

Estimation methods

Statistical tables

Area below the standard normal distribution

Right tail critical values for the t-distribution

Right tail critical value of the Chi-Square distribution

Right tail critical for the F-distribution: 5 percent level

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